Update

Notebooks/sample.ipynb

@@ -160,6 +160,96 @@
     "collapsed": false
    },
    "id": "162b263160a53be2"
+  },
+  {
+   "cell_type": "code",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*********************100%%**********************]  1 of 1 completed\n",
+      "[*********************100%%**********************]  1 of 1 completed\n",
+      "[*********************100%%**********************]  1 of 1 completed\n",
+      "\n",
+      "1 Failed download:\n",
+      "['BHUSANSTL.NS']: Exception('%ticker%: No price data found, symbol may be delisted (1d 2023-01-01 -> 2024-01-01)')\n",
+      "[*********************100%%**********************]  1 of 1 completed"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                           AAPL       GOOG        MSFT\n",
+      "Date                                                  \n",
+      "2023-01-03 00:00:00  124.216301  89.699997  237.036026\n",
+      "2023-01-04 00:00:00  125.497498  88.709999  226.667297\n",
+      "2023-01-05 00:00:00  124.166634  86.769997  219.949371\n",
+      "2023-01-06 00:00:00  128.735229  88.160004  222.541565\n",
+      "2023-01-09 00:00:00  129.261612  88.800003  224.708313\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import yfinance as yf\n",
+    "import pandas as pd\n",
+    "\n",
+    "def fetch_adj_close(ticker):\n",
+    "    try:\n",
+    "        data = yf.download(ticker, start=\"2023-01-01\", end=\"2024-01-01\")\n",
+    "        adj_close = data['Adj Close']  # Extracting adjusted close prices\n",
+    "        adj_close = pd.DataFrame(adj_close)  # Convert Series to DataFrame\n",
+    "        adj_close.columns = [ticker]  # Rename column to ticker symbol\n",
+    "        return adj_close\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error fetching data for {ticker}: {e}\")\n",
+    "        return None\n",
+    "\n",
+    "# List of tickers you want to fetch data for\n",
+    "tickers = [\"AAPL\", \"GOOG\", \"BHUSANSTL.NS\", \"MSFT\"]\n",
+    "\n",
+    "# Create an empty DataFrame to store the adjusted close prices\n",
+    "adj_close_df = pd.DataFrame()\n",
+    "\n",
+    "for ticker in tickers:\n",
+    "    data = fetch_adj_close(ticker)\n",
+    "    if data is not None:\n",
+    "        adj_close_df = pd.concat([adj_close_df, data], axis=1)  # Concatenate the data to the DataFrame\n",
+    "\n",
+    "# Set the date as the index\n",
+    "adj_close_df.index.name = 'Date'\n",
+    "\n",
+    "# Drop columns with any NaN values (i.e., for invalid tickers)\n",
+    "adj_close_df.dropna(axis=1, how='any', inplace=True)\n",
+    "\n",
+    "print(adj_close_df.head())\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-03-08T23:14:00.320054Z",
+     "start_time": "2024-03-08T23:14:00.259300Z"
+    }
+   },
+   "id": "fdb2a5f6391b0ca2",
+   "execution_count": 5
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "b34afac23ee07c5e"
   }
  ],
  "metadata": {

main.py

@@ -38,10 +38,14 @@ company_name = list_df["Name"].to_list()
 company_symbol = (list_df["Ticker"] + ".NS").to_list()
 
 company_dict = dict()
+company_symbol_dict = dict()
 
 for CSymbol, CName in zip(company_symbol, company_name):
     company_dict[CName] = CSymbol
 
+for CSymbol, CName in zip(company_symbol, company_name):
+    company_symbol_dict[CSymbol] = CName
+
 st.markdown(
     """ 
     <style>
@@ -63,7 +67,14 @@ num_tick = len(com_sel)
 if num_tick > 1:
 
     com_data = yf.download(com_sel, start="1900-01-01", end="2024-03-08")["Adj Close"]
+    for i in com_data.columns:
+        com_data.dropna(axis=1, how='all', inplace=True)
     com_data.dropna(inplace=True)
+    num_tick = len(com_data.columns)
+
+    com_sel_name_temp = []
+    for i in com_data.columns:
+        com_sel_name_temp.append(company_symbol_dict[i])
 
     com_sel = com_data.columns.to_list()
     com_sel_name.sort()
@@ -91,7 +102,7 @@ if num_tick > 1:
     # Put the weights into a data frame to see them better.
     weights_df = pd.DataFrame(
         data={
-            "company_name": com_sel_name,
+            "company_name": com_sel_name_temp,
             "random_weights": rand_weig,
             "rebalance_weights": rebal_weig,
         }
@@ -187,7 +198,7 @@ if num_tick > 1:
 
     max_sharpe_weights_df = pd.DataFrame(
         data={
-            "company_name": com_sel_name,
+            "company_name": com_sel_name_temp,
             "random_weights": max_sharpe_ratio["Portfolio Weights"],
         }
     )
@@ -201,7 +212,7 @@ if num_tick > 1:
 
     min_volatility_weights_df = pd.DataFrame(
         data={
-            "company_name": com_sel_name,
+            "company_name": com_sel_name_temp,
             "random_weights": min_volatility["Portfolio Weights"],
         }
     )